<?xml version="1.0" encoding="UTF-8" ?>
<volume id="W17">
  <paper id="7800" href="https://doi.org/10.26615/978-954-452-040-3_">
    <title>Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with RANLP 2017</title>
    <editor>Kalliopi Zervanou</editor>
    <editor>Petya Osenova</editor>
    <editor><first>Austria</first><last>Eveline Wandl-Vogt, Austrian Academy of Sciences</last></editor>
    <editor><first>Romania</first><last>Dan Cristea, "Alexandru Ioan Cuza" University of Iasi</last></editor>
    <month>September</month>
    <year>2017</year>
    <address>Varna</address>
    <publisher>INCOMA Inc.</publisher>
    <doi>10.26615/978-954-452-040-3_</doi>
    <url>https://doi.org/10.26615/978-954-452-040-3_</url>
    <bibtype>book</bibtype>
    <bibkey>KnowRSH:2017</bibkey>
  </paper>

  <paper id="7801" href="https://doi.org/10.26615/978-954-452-040-3_001">
    <title>Connecting people digitally - a semantic web based approach to linking heterogeneous data sets</title>
    <author><first>Katalin</first><last>Lejtovicz</last></author>
    <author><first>Amelie</first><last>Dorn</last></author>
    <booktitle>Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with RANLP 2017</booktitle>
    <month>September</month>
    <year>2017</year>
    <address>Varna</address>
    <publisher>INCOMA Inc.</publisher>
    <pages>1&#8211;8</pages>
    <doi>10.26615/978-954-452-040-3_001</doi>
    <url>https://doi.org/10.26615/978-954-452-040-3_001</url>
    <abstract>In this paper we present a semantic enrichment approach for linking two
	distinct data sets: the &#214;BL (Austrian Biographical Dictionary) and the DB&#214;
	(Database of
	Bavarian Dialects in Austria). Although the data sets are different in their
	content and in the structuring of data, they contain similar common
	"entities" such as names of persons. Here we describe the semantic
	enrichment process of how these data sets can be inter-linked through URIs
	(Uniform Resource Identifiers) 
	taking person names as a concrete example. Moreover, we also point to societal 
	benefits of applying such semantic enrichment methods in order to open and 
	connect our resources to various services.</abstract>
    <bibtype>inproceedings</bibtype>
    <bibkey>lejtovicz-dorn:2017:KnowRSH</bibkey>
  </paper>

  <paper id="7802" href="https://doi.org/10.26615/978-954-452-040-3_002">
    <title>A Multiform Balanced Dependency Treebank for Romanian</title>
    <author><first>Mihaela</first><last>Colhon</last></author>
    <author><first>C&#x103;t&#x103;lina</first><last>M&#x103;r&#x103;nduc</last></author>
    <author><first>C&#x103;t&#x103;lin</first><last>Mititelu</last></author>
    <booktitle>Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with RANLP 2017</booktitle>
    <month>September</month>
    <year>2017</year>
    <address>Varna</address>
    <publisher>INCOMA Inc.</publisher>
    <pages>9&#8211;18</pages>
    <doi>10.26615/978-954-452-040-3_002</doi>
    <url>https://doi.org/10.26615/978-954-452-040-3_002</url>
    <abstract>The UAIC-RoDia-DepTb is a balanced treebank, containing texts in non-standard
	language: 2,575 chats sentences, old Romanian texts (a Gospel printed in 1648,
	a codex of laws printed in 1818, a novel written in 1910), regional popular
	poetry, legal texts, Romanian and foreign fiction, quotations. The proportions
	are comparable; each of these types of texts is represented by subsets of at
	least 1,000 phrases, so that the parser can be trained on their peculiarities.
	The annotation of the treebank started in 2007, and it has classical tags, such
	as those in school grammar, with the intention of using the resource for
	didactic purposes. The classification of circumstantial modifiers is rich in
	semantic information. We present in this paper the development in progress of
	this resource which has been automatically annotated and entirely manually
	corrected. We try to add new texts, and to make it available in more formats,
	by keeping all the morphological and syntactic information annotated, and
	adding logical-semantic information. We will describe here two conversions,
	from the classic syntactic format into Universal Dependencies format and into a
	logical-semantic layer, which will be shortly presented.</abstract>
    <bibtype>inproceedings</bibtype>
    <bibkey>colhon-mvarvanduc-mititelu:2017:KnowRSH</bibkey>
  </paper>

  <paper id="7803" href="https://doi.org/10.26615/978-954-452-040-3_003">
    <title>GRaSP: Grounded Representation and Source Perspective</title>
    <author><first>Antske</first><last>Fokkens</last></author>
    <author><first>Piek</first><last>Vossen</last></author>
    <author><first>Marco</first><last>Rospocher</last></author>
    <author><first>Rinke</first><last>Hoekstra</last></author>
    <author><first>Willem Robert</first><last>van Hage</last></author>
    <booktitle>Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with RANLP 2017</booktitle>
    <month>September</month>
    <year>2017</year>
    <address>Varna</address>
    <publisher>INCOMA Inc.</publisher>
    <pages>19&#8211;25</pages>
    <doi>10.26615/978-954-452-040-3_003</doi>
    <url>https://doi.org/10.26615/978-954-452-040-3_003</url>
    <abstract>When people or organizations provide information, they make choices regarding
	what information they include and how they present it. The combination of these
	two aspects (the content and stance provided by the source) represents a
	perspective. Investigating differences in perspective can provide
	various useful insights in the reliability of information, the way perspectives
	change over time, shared beliefs among groups of a similar social or political
	background and contrasts between other groups, etc. This paper introduces
	GRaSP, a generic framework for modeling perspectives and their sources.</abstract>
    <bibtype>inproceedings</bibtype>
    <bibkey>fokkens-EtAl:2017:KnowRSH</bibkey>
  </paper>

  <paper id="7804" href="https://doi.org/10.26615/978-954-452-040-3_004">
    <title>Educational Content Generation for Business and Administration FL Courses with the NBU PLT Platform</title>
    <author><first>Maria</first><last>Stambolieva</last></author>
    <booktitle>Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with RANLP 2017</booktitle>
    <month>September</month>
    <year>2017</year>
    <address>Varna</address>
    <publisher>INCOMA Inc.</publisher>
    <pages>26&#8211;30</pages>
    <doi>10.26615/978-954-452-040-3_004</doi>
    <url>https://doi.org/10.26615/978-954-452-040-3_004</url>
    <abstract>The paper presents part of an ongoing project of the Laboratory for Language
	Technologies of New Bulgarian University &#8211; "An e-Platform for Language
	Teaching (PLT)" &#8211; the development of corpus-based teaching content for
	Business English courses. The presentation offers information on: 1/ corpus
	creation and corpus management with PLT; 2/ PLT corpus annotation; 3/ language
	task generation and the Language Task Bank (LTB); 4/ content transfer to the
	NBU Moodle platform, test generation and feedback on student performance.</abstract>
    <bibtype>inproceedings</bibtype>
    <bibkey>stambolieva:2017:KnowRSH</bibkey>
  </paper>

  <paper id="7805" href="https://doi.org/10.26615/978-954-452-040-3_005">
    <title>Machine Learning Models of Universal Grammar Parameter Dependencies</title>
    <author><first>Dimitar</first><last>Kazakov</last></author>
    <author><first>Guido</first><last>Cordoni</last></author>
    <author><first>Andrea</first><last>Ceolin</last></author>
    <author><first>Monica-Alexandrina</first><last>Irimia</last></author>
    <author><first>Shin-Sook</first><last>Kim</last></author>
    <author><first>Dimitris</first><last>Michelioudakis</last></author>
    <author><first>Nina</first><last>Radkevich</last></author>
    <author><first>Cristina</first><last>Guardiano</last></author>
    <author><first>Giuseppe</first><last>Longobardi</last></author>
    <booktitle>Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with RANLP 2017</booktitle>
    <month>September</month>
    <year>2017</year>
    <address>Varna</address>
    <publisher>INCOMA Inc.</publisher>
    <pages>31&#8211;37</pages>
    <doi>10.26615/978-954-452-040-3_005</doi>
    <url>https://doi.org/10.26615/978-954-452-040-3_005</url>
    <abstract>The use of parameters in the description of natural language syntax has to
	balance between the need to discriminate among (sometimes subtly different)
	languages, which can be seen as a cross-linguistic version of Chomsky's (1964)
	descriptive adequacy, and the complexity of the acquisition task that a large
	number of parameters would imply, which is a problem for explanatory adequacy.
	Here we present a novel approach in which a machine learning algorithm is used
	to find dependencies in a table of parameters. The result is a dependency graph
	in which some of the parameters can be fully predicted from others. These
	empirical findings can be then subjected to linguistic analysis, which may
	either refute them by providing typological counter-examples of languages not
	included in the original dataset, dismiss them on theoretical grounds, or
	uphold them as tentative empirical laws worth of further study.</abstract>
    <bibtype>inproceedings</bibtype>
    <bibkey>kazakov-EtAl:2017:KnowRSH</bibkey>
  </paper>

</volume>

